Accurately predicting the Dynamic Line Rating of overhead transmission lines is crucial for ensuring safe line capacity expansion. Traditional prediction models, which rely on manual experience for selecting hyperparameters, often struggle to effectively reduce the volatility of DLR, leading to suboptimal prediction accuracy. To address this issue, this study innovatively proposes an SSA-VMD-LSTM-based prediction method. This approach deeply integrates the global optimization capability of the Sparrow Search Algorithm, the multi-scale data decomposition characteristics of Variational Mode Decomposition, and the temporal modeling advantages of Long Short-Term Memory networks, constructing a hierarchical artificial intelligence prediction model. First, the powerful search ability of SSA is employed to iteratively optimize the hyperparameters of VMD, obtaining the optimal hyperparameters. Subsequently, VMD is used to decompose the DLR data into multiple scales, yielding a series of components with different central frequencies but local stationarity. On this basis, separate LSTM models are established to predict each component. Finally, the prediction results of all components are aggregated to produce the final prediction. Experimental results demonstrate that, compared to several traditional prediction models, the proposed method achieves at least a 4.78% improvement in prediction accuracy, fully validating its effectiveness and superiority in DLR prediction.